{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:28:02Z","timestamp":1770287282937,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006199","name":"Langley Research Center","doi-asserted-by":"publisher","award":["NNX14AQ74A"],"award-info":[{"award-number":["NNX14AQ74A"]}],"id":[{"id":"10.13039\/100006199","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10845-023-02255-5","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T11:02:09Z","timestamp":1700737329000},"page":"491-508","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Machine learning guided design of experiments to accelerate exploration of a material extrusion process parameter space"],"prefix":"10.1007","volume":"36","author":[{"given":"Devin","family":"Young","sequence":"first","affiliation":[]},{"given":"Britannia","family":"Vondrasek","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8132-2041","authenticated-orcid":false,"given":"Michael W.","family":"Czabaj","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,23]]},"reference":[{"key":"2255_CR1","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.addma.2017.11.002","volume":"19","author":"AC Abbott","year":"2018","unstructured":"Abbott, A. C., Tandon, G. P., Bradford, R. L., Koerner, H., & Baur, J. W. (2018). Process\u2013structure\u2013property effects on ABS bond strength in fused filament fabrication. Additive Manufacturing, 19, 29\u201338. https:\/\/doi.org\/10.1016\/j.addma.2017.11.002","journal-title":"Additive Manufacturing"},{"issue":"4","key":"2255_CR2","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1108\/13552540210441166","volume":"8","author":"S-H Ahn","year":"2002","unstructured":"Ahn, S.-H., Montero, M., Odell, D., Roundy, S., & Wright, P. K. (2002). Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyping Journal, 8(4), 248\u2013257. https:\/\/doi.org\/10.1108\/13552540210441166","journal-title":"Rapid Prototyping Journal"},{"key":"2255_CR3","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1016\/j.jmapro.2021.09.057","volume":"71","author":"A Alafaghani","year":"2021","unstructured":"Alafaghani, A., Ablat, M. H., Abedi, H., & Qattawi, A. (2021). Modeling the influence of fused filament fabrication processing parameters on the mechanical properties of ABS parts. Journal of Manufacturing Processes, 71, 711\u2013723. https:\/\/doi.org\/10.1016\/j.jmapro.2021.09.057","journal-title":"Journal of Manufacturing Processes"},{"key":"2255_CR4","unstructured":"ASTM. (2021). ASTM D5528\/D5528-21 standard test method for Mode I interlaminar fracture toughness of unidirectional fiber-reinforced polymer matrix composites. ASTM."},{"key":"2255_CR5","unstructured":"Bagsik, A., Sch\u00f6ppner, V., & Klemp, E. (2010). FDM part quality manufactured with Ultem* 9085. In 14th International scientific conference on polymeric materials, 2010 (Vol. 15, pp. 307\u2013315)."},{"issue":"1","key":"2255_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Machine Learning"},{"key":"2255_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101218","volume":"34","author":"A Das","year":"2020","unstructured":"Das, A., Chatham, C. A., Fallon, J. J., Zawaski, C. E., Gilmer, E. L., Williams, C. B., & Bortner, M. J. (2020). Current understanding and challenges in high temperature additive manufacturing of engineering thermoplastic polymers. Additive Manufacturing, 34, 101218. https:\/\/doi.org\/10.1016\/j.addma.2020.101218","journal-title":"Additive Manufacturing"},{"issue":"2","key":"2255_CR8","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1063\/1.1675789","volume":"55","author":"P-G De Gennes","year":"1971","unstructured":"De Gennes, P.-G. (1971). Reptation of a polymer chain in the presence of fixed obstacles. The Journal of Chemical Physics, 55(2), 572\u2013579. https:\/\/doi.org\/10.1063\/1.1675789","journal-title":"The Journal of Chemical Physics"},{"key":"2255_CR9","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.compstruct.2019.02.005","volume":"214","author":"J Fonseca","year":"2019","unstructured":"Fonseca, J., Ferreira, I. A., De Moura, M. F. S. F., Machado, M., & Alves, J. L. (2019). Study of the interlaminar fracture under mode I loading on FFF printed parts. Composite Structures, 214, 316\u2013324. https:\/\/doi.org\/10.1016\/j.compstruct.2019.02.005","journal-title":"Composite Structures"},{"key":"2255_CR10","doi-asserted-by":"crossref","unstructured":"Forster, A. M. (2015). Materials testing standards for additive manufacturing of polymer materials (p. 8059). US Department of Commerce, National Institute of Standards and Technology.","DOI":"10.6028\/NIST.IR.8059"},{"key":"2255_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101658","volume":"37","author":"X Gao","year":"2021","unstructured":"Gao, X., Qi, S., Kuang, X., Su, Y., Li, J., & Wang, D. (2021). Fused filament fabrication of polymer materials: A review of interlayer bond. Additive Manufacturing, 37, 101658. https:\/\/doi.org\/10.1016\/j.addma.2020.101658","journal-title":"Additive Manufacturing"},{"key":"2255_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2021.102412","volume":"48","author":"EL Gilmer","year":"2021","unstructured":"Gilmer, E. L., Anderegg, D., Gardner, J. M., Sauti, G., Siochi, E. J., McKnight, S. H., Dillard, D. A., McIlroy, C., & Bortner, M. J. (2021). Temperature, diffusion, and stress modeling in filament extrusion additive manufacturing of polyetherimide: An examination of the influence of processing parameters and importance of modeling assumptions. Additive Manufacturing, 48, 102412. https:\/\/doi.org\/10.1016\/j.addma.2021.102412","journal-title":"Additive Manufacturing"},{"key":"2255_CR14","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.polymer.2018.04.024","volume":"144","author":"KR Hart","year":"2018","unstructured":"Hart, K. R., Dunn, R. M., Sietins, J. M., Mock, C. M. H., Mackay, M. E., & Wetzel, E. D. (2018). Increased fracture toughness of additively manufactured amorphous thermoplastics via thermal annealing. Polymer, 144, 192\u2013204. https:\/\/doi.org\/10.1016\/j.polymer.2018.04.024","journal-title":"Polymer"},{"key":"2255_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.engfracmech.2017.03.028","volume":"177","author":"KR Hart","year":"2017","unstructured":"Hart, K. R., & Wetzel, E. D. (2017). Fracture behavior of additively manufactured acrylonitrile butadiene styrene (ABS) materials. Engineering Fracture Mechanics, 177, 1\u201313. https:\/\/doi.org\/10.1016\/j.engfracmech.2017.03.028","journal-title":"Engineering Fracture Mechanics"},{"issue":"4","key":"2255_CR15","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1007\/s10845-020-01715-6","volume":"33","author":"J Jiang","year":"2022","unstructured":"Jiang, J., Xiong, Y., Zhang, Z., & Rosen, D. W. (2022). Machine learning integrated design for additive manufacturing. Journal of Intelligent Manufacturing, 33(4), 1073\u20131086. https:\/\/doi.org\/10.1007\/s10845-020-01715-6","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"12","key":"2255_CR16","doi-asserted-by":"publisher","first-page":"1599","DOI":"10.1177\/0892705719874860","volume":"34","author":"AS Khan","year":"2021","unstructured":"Khan, A. S., Ali, A., Hussain, G., & Ilyas, M. (2021). An experimental study on interfacial fracture toughness of 3-D printed ABS\/CF-PLA composite under mode I, II, and mixed-mode loading. Journal of Thermoplastic Composite Materials, 34(12), 1599\u20131622. https:\/\/doi.org\/10.1177\/0892705719874860","journal-title":"Journal of Thermoplastic Composite Materials"},{"key":"2255_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.tafmec.2020.102763","volume":"109","author":"MR Khosravani","year":"2020","unstructured":"Khosravani, M. R., Berto, F., Ayatollahi, M. R., & Reinicke, T. (2020). Fracture behavior of additively manufactured components: A review. Theoretical and Applied Fracture Mechanics, 109, 102763. https:\/\/doi.org\/10.1016\/j.tafmec.2020.102763","journal-title":"Theoretical and Applied Fracture Mechanics"},{"key":"2255_CR18","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.addma.2019.05.006","volume":"28","author":"A Khudiakova","year":"2019","unstructured":"Khudiakova, A., Arbeiter, F., Spoerk, M., Wolfahrt, M., Godec, D., & Pinter, G. (2019). Inter-layer bonding characterisation between materials with different degrees of stiffness processed by fused filament fabrication. Additive Manufacturing, 28, 184\u2013193. https:\/\/doi.org\/10.1016\/j.addma.2019.05.006","journal-title":"Additive Manufacturing"},{"key":"2255_CR19","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.addma.2017.06.003","volume":"16","author":"C Koch","year":"2017","unstructured":"Koch, C., Van Hulle, L., & Rudolph, N. (2017). Investigation of mechanical anisotropy of the fused filament fabrication process via customized tool path generation. Additive Manufacturing, 16, 138\u2013145. https:\/\/doi.org\/10.1016\/j.addma.2017.06.003","journal-title":"Additive Manufacturing"},{"issue":"1","key":"2255_CR20","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s10845-022-02029-5","volume":"34","author":"S Kumar","year":"2023","unstructured":"Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M. K., Gaur, V., Krolczyk, G. M., & Wu, C. (2023). Machine learning techniques in additive manufacturing: A state of the art review on design, processes and production control. Journal of Intelligent Manufacturing, 34(1), 21\u201355. https:\/\/doi.org\/10.1007\/s10845-022-02029-5","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2255_CR21","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.addma.2018.11.012","volume":"25","author":"C-Y Lee","year":"2019","unstructured":"Lee, C.-Y., & Liu, C.-Y. (2019). The influence of forced-air cooling on a 3D printed PLA part manufactured by fused filament fabrication. Additive Manufacturing, 25, 196\u2013203. https:\/\/doi.org\/10.1016\/j.addma.2018.11.012","journal-title":"Additive Manufacturing"},{"issue":"3","key":"2255_CR22","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s40192-017-0098-z","volume":"6","author":"J Ling","year":"2017","unstructured":"Ling, J., Hutchinson, M., Antono, E., Paradiso, S., & Meredig, B. (2017). High-dimensional materials and process optimization using data-driven experimental design with well-calibrated uncertainty estimates. Integrating Materials and Manufacturing Innovation, 6(3), 207\u2013217. https:\/\/doi.org\/10.1007\/s40192-017-0098-z","journal-title":"Integrating Materials and Manufacturing Innovation"},{"issue":"6","key":"2255_CR23","doi-asserted-by":"publisher","first-page":"2944","DOI":"10.1109\/TMECH.2020.3049046","volume":"26","author":"K Liu","year":"2021","unstructured":"Liu, K., Hu, X., Zhou, H., Tong, L., Widanage, W. D., & Marco, J. (2021). Feature analyses and modeling of lithium-ion battery manufacturing based on random forest classification. IEEE\/ASME Transactions on Mechatronics, 26(6), 2944\u20132955. https:\/\/doi.org\/10.1109\/TMECH.2020.3049046","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"issue":"6","key":"2255_CR24","doi-asserted-by":"publisher","first-page":"2363","DOI":"10.1007\/s11837-020-04155-y","volume":"72","author":"L Meng","year":"2020","unstructured":"Meng, L., McWilliams, B., Jarosinski, W., Park, H. Y., Jung, Y. G., Lee, J., & Zhang, J. (2020). Machine learning in additive manufacturing: A review. JOM Journal of the Minerals, Metals and Materials Society, 72(6), 2363\u20132377. https:\/\/doi.org\/10.1007\/s11837-020-04155-y","journal-title":"JOM Journal of the Minerals, Metals and Materials Society"},{"key":"2255_CR25","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1016\/j.jmrt.2021.07.004","volume":"14","author":"S Nasiri","year":"2021","unstructured":"Nasiri, S., & Khosravani, M. R. (2021). Machine learning in predicting mechanical behavior of additively manufactured parts. Journal of Materials Research and Technology, 14, 1137\u20131153. https:\/\/doi.org\/10.1016\/j.jmrt.2021.07.004","journal-title":"Journal of Materials Research and Technology"},{"issue":"02","key":"2255_CR26","doi-asserted-by":"publisher","first-page":"89","DOI":"10.4236\/iim.2009.12014","volume":"1","author":"SK Panda","year":"2009","unstructured":"Panda, S. K., Padhee, S., Sood, A. P., & Mahapatra, S. S. (2009). Optimization of fused deposition modelling (FDM) process parameters using bacterial foraging technique. Intelligent Information Management, 1(02), 89\u201397. https:\/\/doi.org\/10.4236\/iim.2009.12014","journal-title":"Intelligent Information Management"},{"issue":"1","key":"2255_CR27","doi-asserted-by":"publisher","first-page":"R19","DOI":"10.1007\/BF00034281","volume":"38","author":"AJ Paris","year":"1988","unstructured":"Paris, A. J., & Paris, P. C. (1988). Instantaneous evaluation of J and C. International Journal of Fracture, 38(1), R19\u2013R21. https:\/\/doi.org\/10.1007\/BF00034281","journal-title":"International Journal of Fracture"},{"key":"2255_CR28","doi-asserted-by":"publisher","unstructured":"Rodriguez, J. F., Thomas, J. P., & Renaud, J. E. (1999). Maximizing the strength of fused-deposition ABS plastic parts. In 1999 International solid freeform fabrication symposium, 1999. https:\/\/doi.org\/10.26153\/tsw\/786","DOI":"10.26153\/tsw\/786"},{"issue":"8","key":"2255_CR29","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1016\/j.polymertesting.2013.07.014","volume":"32","author":"WC Smith","year":"2013","unstructured":"Smith, W. C., & Dean, R. W. (2013). Structural characteristics of fused deposition modeling polycarbonate material. Polymer Testing, 32(8), 1306\u20131312. https:\/\/doi.org\/10.1016\/j.polymertesting.2013.07.014","journal-title":"Polymer Testing"},{"issue":"1","key":"2255_CR30","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.jare.2011.05.001","volume":"3","author":"AK Sood","year":"2012","unstructured":"Sood, A. K., Ohdar, R. K., & Mahapatra, S. S. (2012). Experimental investigation and empirical modelling of FDM process for compressive strength improvement. Journal of Advanced Research, 3(1), 81\u201390. https:\/\/doi.org\/10.1016\/j.jare.2011.05.001","journal-title":"Journal of Advanced Research"},{"issue":"41","key":"2255_CR31","doi-asserted-by":"publisher","first-page":"45401","DOI":"10.1002\/app.45401","volume":"134","author":"M Spoerk","year":"2017","unstructured":"Spoerk, M., Arbeiter, F., Cajner, H., Sapkota, J., & Holzer, C. (2017). Parametric optimization of intra- and inter-layer strengths in parts produced by extrusion-based additive manufacturing of poly (lactic acid). Journal of Applied Polymer Science, 134(41), 45401. https:\/\/doi.org\/10.1002\/app.45401","journal-title":"Journal of Applied Polymer Science"},{"issue":"2","key":"2255_CR32","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1108\/13552540810862028","volume":"14","author":"Q Sun","year":"2008","unstructured":"Sun, Q., Rizvi, G. M., Bellehumeur, C. T., & Gu, P. (2008). Effect of processing conditions on the bonding quality of FDM polymer filaments. Rapid Prototyping Journal, 14(2), 72\u201380. https:\/\/doi.org\/10.1108\/13552540810862028","journal-title":"Rapid Prototyping Journal"},{"key":"2255_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101538","volume":"36","author":"C Wang","year":"2020","unstructured":"Wang, C., Tan, X. P., Tor, S. B., & Lim, C. S. (2020). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 36, 101538. https:\/\/doi.org\/10.1016\/j.addma.2020.101538","journal-title":"Additive Manufacturing"},{"issue":"10","key":"2255_CR34","doi-asserted-by":"publisher","first-page":"5953","DOI":"10.1063\/1.328526","volume":"52","author":"R Wool","year":"1981","unstructured":"Wool, R., & O\u2019Connor, K. M. (1981). A theory crack healing in polymers. Journal of Applied Physics, 52(10), 5953\u20135963. https:\/\/doi.org\/10.1063\/1.328526","journal-title":"Journal of Applied Physics"},{"key":"2255_CR36","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.jmapro.2021.04.007","volume":"66","author":"D Wu","year":"2021","unstructured":"Wu, D., Hu, M., Huang, Y., Zhang, P., & Yu, Z. (2021). In situ monitoring and penetration prediction of plasma arc welding based on welder intelligence-enhanced deep random forest fusion. Journal of Manufacturing Processes, 66, 153\u2013165. https:\/\/doi.org\/10.1016\/j.jmapro.2021.04.007","journal-title":"Journal of Manufacturing Processes"},{"issue":"7","key":"2255_CR35","doi-asserted-by":"publisher","DOI":"10.1115\/1.4036350","volume":"139","author":"D Wu","year":"2017","unstructured":"Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017). A comparative study on machine learning algorithms for smart manufacturing: Tool wear prediction using random forests. Journal of Manufacturing Science and Engineering, 139(7), 071018. https:\/\/doi.org\/10.1115\/1.4036350","journal-title":"Journal of Manufacturing Science and Engineering"},{"key":"2255_CR37","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.addma.2018.02.023","volume":"22","author":"D Young","year":"2018","unstructured":"Young, D., Wetmore, N., & Czabaj, M. (2018). Interlayer fracture toughness of additively manufactured unreinforced and carbon-fiber-reinforced acrylonitrile butadiene styrene. Additive Manufacturing, 22, 508\u2013515. https:\/\/doi.org\/10.1016\/j.addma.2018.02.023","journal-title":"Additive Manufacturing"},{"key":"2255_CR38","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.addma.2016.11.007","volume":"13","author":"RJ Zaldivar","year":"2017","unstructured":"Zaldivar, R. J., Witkin, D. B., McLouth, T., Patel, D. N., Schmitt, K., & Nokes, J. P. (2017). Influence of processing and orientation print effects on the mechanical and thermal behavior of 3D-Printed ULTEM\u00ae 9085 Material. Additive Manufacturing, 13, 71\u201380. https:\/\/doi.org\/10.1016\/j.addma.2016.11.007","journal-title":"Additive Manufacturing"},{"issue":"4","key":"2255_CR39","doi-asserted-by":"publisher","first-page":"2013","DOI":"10.1007\/s10845-021-01894-w","volume":"34","author":"M Zhu","year":"2023","unstructured":"Zhu, M., Yang, Y., Feng, X., Du, Z., & Yang, J. (2023). Robust modeling method for thermal error of CNC machine tools based on random forest algorithm. Journal of Intelligent Manufacturing, 34(4), 2013\u20132026. https:\/\/doi.org\/10.1007\/s10845-021-01894-w","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"512","key":"2255_CR40","doi-asserted-by":"publisher","first-page":"1770","DOI":"10.1080\/01621459.2015.1036994","volume":"110","author":"R Zhu","year":"2015","unstructured":"Zhu, R., Zeng, D., & Kosorok, M. R. (2015). Reinforcement learning trees. Journal of the American Statistical Association, 110(512), 1770\u20131784. https:\/\/doi.org\/10.1080\/01621459.2015.1036994","journal-title":"Journal of the American Statistical Association"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02255-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02255-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02255-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T23:16:20Z","timestamp":1736032580000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02255-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,23]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["2255"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02255-5","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,23]]},"assertion":[{"value":"14 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}